{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "5LZkDfavuZfH"
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "import numpy as np\n",
    "import yfinance as yf\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.layers import LSTM, Dense, Dropout, Conv1D, MaxPooling1D, Flatten\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score  # 导入评估指标函数\n",
    "import matplotlib.pyplot as plt  # 导入数据可视化库 Matplotlib\n",
    "import yfinance as yf\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import pickle\n",
    "from tqdm.notebook import tnrange\n",
    "import seaborn as sns\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3Bh1CjNHuefO"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "v2DrT_Fluhmo"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import pickle\n",
    "from tqdm.notebook import tnrange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9tgxxH2ZunW4",
    "outputId": "e600c7ea-d8db-40e7-e9aa-5ad4ccd574ea"
   },
   "outputs": [],
   "source": [
    "# scale train and test data to new feature range[0, 1]将训练和测试数据缩放到新的特征范围[0，1]\n",
    "def scale(train, test):\n",
    "\t# fit scaler\n",
    "\tscaler = MinMaxScaler(feature_range=(0, 1))\n",
    "\tscaler = scaler.fit(train)\n",
    "\t# transform train\n",
    "\ttrain = train.reshape(train.shape[0], train.shape[1])\n",
    "\ttrain_scaled = scaler.transform(train)\n",
    "\t# transform test\n",
    "\ttest = test.reshape(test.shape[0], test.shape[1])\n",
    "\ttest_scaled = scaler.transform(test)\n",
    "\treturn scaler, train_scaled, test_scaled\n",
    "\n",
    "# compute wMAPE weighted absolute percentage error计算wMAPE加权绝对百分比误差\n",
    "def wMAPE(actual, predicted): \n",
    "    result_nom = 0\n",
    "    result_deno = 0\n",
    "    for i in range(len(actual)):\n",
    "        result_nom +=  abs(actual[i] - predicted[i])\n",
    "        result_deno +=  abs(actual[i]) \n",
    "    result = result_nom/result_deno\n",
    "    return result *100\n",
    "\n",
    "def scaled(X, y):\n",
    "    max_train = np.max(X, axis=0) #标准化\n",
    "    min_train = np.min(X, axis=0)\n",
    "    X = 0.0 + (X - min_train) / (max_train - min_train)\n",
    "    max_targets = np.max(y, axis=0)\n",
    "    min_targets = np.min(y, axis=0)\n",
    "    y = 0.0 + (y - min_targets) / (max_targets - min_targets)\n",
    "    return X, y, max_train, min_train, max_targets, min_targets\n",
    "\n",
    "def inscaled(ypred):\n",
    "    y_pred = ypred * (max_targets - min_targets) + min_targets\n",
    "    return y_pred\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IX7fxTzQviVT",
    "outputId": "e0a865de-e3a3-42ef-94ec-2ede85635bd2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       radiation  air temperature  air pressure  humidity  actual power/MW\n",
      "0            0.0            -14.1       908.672    81.997              0.0\n",
      "1            0.0            -14.6       908.672    82.597              0.0\n",
      "2            0.0            -14.4       908.672    82.897              0.0\n",
      "3            0.0            -14.8       908.672    82.997              0.0\n",
      "4            0.0            -14.8       909.172    82.897              0.0\n",
      "...          ...              ...           ...       ...              ...\n",
      "35021        0.0             -4.8       899.600    54.900              0.0\n",
      "35022        0.0             -5.9       900.100    55.800              0.0\n",
      "35023        0.0             -6.5       900.100    57.900              0.0\n",
      "35024        0.0             -7.4       900.100    59.400              0.0\n",
      "35025        0.0             -8.0       900.100    62.700              0.0\n",
      "\n",
      "[35026 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "df = pd.read_csv('PVDATA.csv')\n",
    "# 删除时间列\n",
    "df = df.drop(columns=['TIME'])\n",
    "# 目标列\n",
    "target_column = 'actual power/MW'\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 175
    },
    "id": "VkRMpjH3vsxV",
    "outputId": "0cefe31f-2a9b-4ad4-9a0e-edf704532cfa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0.    -14.1   908.672  81.997]\n",
      " [  0.    -14.6   908.672  82.597]\n",
      " [  0.    -14.4   908.672  82.897]\n",
      " ...\n",
      " [  0.     -6.5   900.1    57.9  ]\n",
      " [  0.     -7.4   900.1    59.4  ]\n",
      " [  0.     -8.    900.1    62.7  ]]\n"
     ]
    }
   ],
   "source": [
    "X = df.drop(columns=target_column).values  # 从 DataFrame `df` 中去除目标列 `target_column`，并将剩余的列转换为数组赋值给变量 `X`\n",
    "\n",
    "y = df[target_column].values  # 从 DataFrame `df` 中选择目标列 `target_column`，并将其转换为数组赋值给变量 `y`\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "AELPEuNEvvIu",
    "outputId": "4580e7bf-d929-4a5b-f844-83a7dd037c09"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.          0.70982043 -0.1581825  -0.25278188  0.63564631]\n",
      " [ 0.70982043  1.         -0.59307447 -0.14555079  0.57492931]\n",
      " [-0.1581825  -0.59307447  1.         -0.0128098  -0.2217157 ]\n",
      " [-0.25278188 -0.14555079 -0.0128098   1.         -0.29023335]\n",
      " [ 0.63564631  0.57492931 -0.2217157  -0.29023335  1.        ]]\n"
     ]
    }
   ],
   "source": [
    "X, y, max_train, min_train, max_targets, min_targets = scaled(X, y)#标准化\n",
    "# 计算皮尔逊相关系数矩阵\n",
    "corr = np.corrcoef(X,y,rowvar=False)\n",
    "print(corr)\n",
    "# 计算每个特征的相关性权重\n",
    "weights = np.abs(corr[-1, :-1])\n",
    "# 对数据进行加权\n",
    "X = np.multiply(X, weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 517
    },
    "id": "9FJ0l2uyv-c_",
    "outputId": "a1529b5a-fa6a-43e1-aceb-6071052092be"
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 3000x2000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_names = [ 'radiation','air temperature', 'air pressure','humidity','actual power/MW']\n",
    "plt.figure(figsize=(30,20))\n",
    "sns.set(font_scale=1.2)\n",
    "sns.heatmap(corr, annot=True, cmap='coolwarm', fmt='.2f', annot_kws={\"size\": 12},\n",
    "            xticklabels=feature_names, yticklabels=feature_names,)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 517
    },
    "id": "GMwtGRGuwAYo",
    "outputId": "6e59772b-496a-4fea-a2d8-4217b1861052"
   },
   "outputs": [],
   "source": [
    "'''定义一个函数用于创建数据集，输入参数包括:\n",
    "dataset: 数据集\n",
    "look_back: 回溯长度，即用多少个时间步作为输入预测下一个时间步'''\n",
    "def creat_dataset(dataset, look_back):\n",
    "    dataX, dataY = [], []\n",
    "    for i in range(len(dataset) - look_back - 1):# 循环遍历数据集\n",
    "        a = dataset[i: (i + look_back)]# 提取回溯长度的数据作为输入\n",
    "        dataX.append(a)# 将该输入数据添加到输入数据集\n",
    "        dataY.append(dataset[i + look_back])# 将下一个时间步的数据添加到输出数据集\n",
    "    return np.array(dataX), np.array(dataY)# 将输入输出数据集转换为numpy数组并返回"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "AF2Q7kW8wGKz"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train X shape: (28020,)\n"
     ]
    }
   ],
   "source": [
    "dataset = y\n",
    "train_size = int(len(dataset) * 0.8)  # 计算训练集大小，将数据集长度的 80% 转换为整数\n",
    "test_size = len(dataset) - train_size  # 计算测试集大小，将剩余的数据作为测试集\n",
    "train, test = dataset[0: train_size], dataset[train_size: len(dataset)]  # 将数据集划分为训练集和测试集，使用切片操作实现\n",
    "print(\"Train X shape:\", train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "tG_r8zTO0LiE"
   },
   "outputs": [],
   "source": [
    "look_back = 2\n",
    "train_X, train_Y = creat_dataset(train, look_back)  # 根据训练集数据和滑动窗口大小创建训练集的输入序列和输出值\n",
    "test_X, test_Y = creat_dataset(test, look_back)  # 根据测试集数据和滑动窗口大小创建测试集的输入序列和输出值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "ads7b1Av0KD5"
   },
   "outputs": [],
   "source": [
    "# 调用GPU加速\n",
    "gpus = tf.config.experimental.list_physical_devices(device_type='GPU')\n",
    "for gpu in gpus:\n",
    "    tf.config.experimental.set_memory_growth(gpu, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "a8cA0UEBwo3N",
    "outputId": "175cefe4-156a-402b-93bc-1d6d98c116fe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train X shape: (28017, 2)\n",
      "Train Y shape: (28017,)\n",
      "Test X shape: (7003, 2)\n",
      "Test Y shape: (7003,)\n"
     ]
    }
   ],
   "source": [
    "# Ensure the sizes of training and testing sets\n",
    "print(\"Train X shape:\", train_X.shape)\n",
    "print(\"Train Y shape:\", train_Y.shape)\n",
    "print(\"Test X shape:\", test_X.shape)\n",
    "print(\"Test Y shape:\", test_Y.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "_mq48nepzY_I"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import ModelCheckpoint , ReduceLROnPlateau\n",
    "\n",
    "\n",
    "save_best = ModelCheckpoint(\"best_weights.h5\", monitor='val_loss', save_best_only=True, save_weights_only=True)\n",
    "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.25,patience=4, min_lr=0.00001,verbose = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "TBrMgLrIwsGv"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KerasTensor(type_spec=TensorSpec(shape=(None, 1), dtype=tf.float32, name=None), name='dense/BiasAdd:0', description=\"created by layer 'dense'\")\n",
      "Epoch 1/50\n",
      "438/438 [==============================] - 4s 4ms/step - loss: 0.0078 - val_loss: 0.0029 - lr: 0.0010\n",
      "Epoch 2/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0048 - val_loss: 0.0029 - lr: 0.0010\n",
      "Epoch 3/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0047 - val_loss: 0.0028 - lr: 0.0010\n",
      "Epoch 4/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0047 - val_loss: 0.0028 - lr: 0.0010\n",
      "Epoch 5/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0048 - val_loss: 0.0028 - lr: 0.0010\n",
      "Epoch 6/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0047 - val_loss: 0.0028 - lr: 0.0010\n",
      "Epoch 7/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0047 - val_loss: 0.0028 - lr: 0.0010\n",
      "Epoch 8/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0047 - val_loss: 0.0030 - lr: 0.0010\n",
      "Epoch 9/50\n",
      "427/438 [============================>.] - ETA: 0s - loss: 0.0047\n",
      "Epoch 9: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0047 - val_loss: 0.0028 - lr: 0.0010\n",
      "Epoch 10/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 2.5000e-04\n",
      "Epoch 11/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 2.5000e-04\n",
      "Epoch 12/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 2.5000e-04\n",
      "Epoch 13/50\n",
      "436/438 [============================>.] - ETA: 0s - loss: 0.0046\n",
      "Epoch 13: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05.\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 2.5000e-04\n",
      "Epoch 14/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 6.2500e-05\n",
      "Epoch 15/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 6.2500e-05\n",
      "Epoch 16/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 6.2500e-05\n",
      "Epoch 17/50\n",
      "426/438 [============================>.] - ETA: 0s - loss: 0.0046\n",
      "Epoch 17: ReduceLROnPlateau reducing learning rate to 1.5625000742147677e-05.\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 6.2500e-05\n",
      "Epoch 18/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 1.5625e-05\n",
      "Epoch 19/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 1.5625e-05\n",
      "Epoch 20/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 1.5625e-05\n",
      "Epoch 21/50\n",
      "432/438 [============================>.] - ETA: 0s - loss: 0.0046\n",
      "Epoch 21: ReduceLROnPlateau reducing learning rate to 1e-05.\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 1.5625e-05\n",
      "Epoch 22/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 23/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 24/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 25/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 26/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 27/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 28/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 29/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 30/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 31/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 32/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 33/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 34/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 35/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 36/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 37/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 38/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 39/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 40/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 41/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 42/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 43/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 44/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 45/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 46/50\n",
      "438/438 [==============================] - 2s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 47/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 48/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 49/50\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 50/50\n",
      "438/438 [==============================] - 2s 3ms/step - loss: 0.0046 - val_loss: 0.0028 - lr: 1.0000e-05\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1ea2cc99190>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.layers import Concatenate, Input\n",
    "from keras.models import Model\n",
    "from tensorflow.keras.layers import Conv1D, LSTM, Bidirectional ,ZeroPadding1D ,BatchNormalization, Add,Layer,Dot\n",
    "# Define the input layer shape for both LSTM and CNN\n",
    "rnn_cell_size = 128\n",
    "inputs=Input(shape=(train_X.shape[1], 1))\n",
    "\n",
    "bilstm = Bidirectional(LSTM(rnn_cell_size,\n",
    "                        return_sequences=True), name=\"bi_lstm_0\")(inputs)\n",
    "Flatten1 =tf.keras.layers.Flatten(name='Flatten')(bilstm)\n",
    "outputs = Dense(1)(Flatten1)\n",
    "print(outputs)\n",
    "model = Model(inputs=inputs, outputs=outputs)\n",
    "\n",
    "model.compile(optimizer='adam', loss='mse')\n",
    "# Train the LSTM model\n",
    "model.fit(train_X, train_Y, epochs=50, batch_size=64,validation_data=(test_X , test_Y),\n",
    "            callbacks=[reduce_lr , save_best])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "876/876 [==============================] - 1s 928us/step\n",
      "219/219 [==============================] - 0s 939us/step\n"
     ]
    }
   ],
   "source": [
    "# 对训练集进行预测\n",
    "ypred_train = model.predict(train_X)\n",
    "# 将训练集的预测结果转换为原始数据的范围\n",
    "ypred_train = inscaled(ypred_train)\n",
    "# 将训练集的真实标签转换为原始数据的范围\n",
    "y_train = inscaled(train_Y)\n",
    "\n",
    "# 对测试集进行预测\n",
    "ypred_test = model.predict(test_X)\n",
    "# 将测试集的预测结果转换为原始数据的范围\n",
    "ypred_test = inscaled(ypred_test)\n",
    "# 将测试集的真实标签转换为原始数据的范围\n",
    "y_test = inscaled(test_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wvkFS4KjwxIY",
    "outputId": "f6839368-9d52-419c-f04c-ce18572bcb8f"
   },
   "outputs": [],
   "source": [
    "y_test = y_test[0:len(ypred_test)-1]\n",
    "ypred_test = ypred_test[1:len(ypred_test),0]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8OGCdy0MwzJm",
    "outputId": "77561502-eb1e-4fe3-edf2-3065c5185729"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0. ... 0. 0. 0.]\n",
      "RMSE 0.400 \n",
      "MAE 0.203 \n",
      "R2 0.996 \n",
      "MAPE 5.606 \n"
     ]
    }
   ],
   "source": [
    "print(y_test)\n",
    "# 计算测试集的均方根误差（RMSE）\n",
    "testScore = math.sqrt(mean_squared_error(y_test, ypred_test))\n",
    "print('RMSE %.3f ' %(testScore))\n",
    "# 计算测试集的平均绝对误差（MAE）\n",
    "testScore = mean_absolute_error(y_test, ypred_test)\n",
    "print('MAE %.3f ' %(testScore))\n",
    "# 计算测试集的R平方值（R2）\n",
    "testScore = r2_score(y_test, ypred_test)\n",
    "print('R2 %.3f ' %(testScore))\n",
    "testScore = wMAPE(y_test, ypred_test)\n",
    "print('MAPE %.3f ' %(testScore))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "4Qke2Du1w4o_"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# 画出测试集预测结果与真实值的对比折线图\n",
    "plt.figure(figsize=(12, 3))\n",
    "# 创建一个图形窗口，设置图形窗口的大小为宽度 12，高度 3\n",
    "plt.plot(y_test[1200:1500], label='True', color='r', linewidth=1) # 绘制 y_test 数据的折线图，并设置标签为 'Actual'\n",
    "plt.plot(ypred_test[1200:1500], label='Predicted', color='b' , linewidth=1, linestyle=\"--\") # 绘制 y_pred 数据的折线图，并设置标签为 'Predicted'\n",
    "plt.title('LSTM-CNN Prediction', size=10)  # 设置图形的标题为'DBO-LSTM Prediction'，字体大小为10\n",
    "plt.ylabel('PV/MW', fontsize=10)  # 设置y轴标签为'PV（kWh）'，字体大小为10\n",
    "plt.xlabel('Sampling points', fontsize=10)  # 设置x轴标签为'Sampling points'，字体大小为10\n",
    "plt.legend()\n",
    "plt.savefig(\"LSTM.png\")\n",
    "# 显示图例\n",
    "plt.show()\n",
    "\n",
    "# 显示图形窗口"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pACCIDAMw7a8",
    "outputId": "d4c67dce-4cd5-4f5d-90c5-ef4806e436a6"
   },
   "outputs": [],
   "source": [
    "import xlrd\n",
    "import xlwt  #对xls文件进行改写\n",
    "from xlutils.copy import copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vtJodmNww988",
    "outputId": "d83f12dc-a917-48f3-ec1a-68870692cafd"
   },
   "outputs": [],
   "source": [
    "# 新建表格\n",
    "def excel_create(path, sheet_name):\n",
    "    workbook = xlwt.Workbook(encoding = 'utf-8',style_compression = 0)  #相当于创建了一个EXCEL文件，style_compression:表示是否压缩，不常用)\n",
    "    workbook.add_sheet(sheet_name)  # 在工作簿中新建一个表格\n",
    "    workbook.save(path)  # 保存工作簿\n",
    "    \n",
    "# 写入表头\n",
    "def excel_write_title(path, title):\n",
    "    workbook = xlrd.open_workbook(path)  # 打开工作簿\n",
    "    new_workbook = copy(workbook)  # 拷贝原工作簿\n",
    "    new_worksheet = new_workbook.get_sheet(0)  # 获取转化后工作簿中的第一个表格，即index=0\n",
    "    for j in range(0, len(title)):\n",
    "        new_worksheet.write(0, j, str(title[j]))  # 在表格中第一行（row=0)写入标题\n",
    "    new_workbook.save(path)  # 保存工作簿\n",
    " \n",
    "# 向表格按列写入一维数组（列表）\n",
    "def excel_write_array(path, value, column):\n",
    "    workbook = xlrd.open_workbook(path)  # 打开工作簿\n",
    "    new_workbook = copy(workbook)  # 拷贝原工作簿\n",
    "    new_worksheet = new_workbook.get_sheet(0)  # 获取转化后工作簿中的第一个表格\n",
    "    for i in range(0, len(value)):\n",
    "        new_worksheet.write(i+1, column, float(value[i]))# 从第二行开始(row=i+1)，按对应的列(column)向表格中写入数据\n",
    "    new_workbook.save(path)  # 保存工作簿\n",
    " \n",
    "# 向表格写入二维数组（列表）\n",
    "def excel_write_array2(path, value):\n",
    "    workbook = xlrd.open_workbook(path)  # 打开工作簿\n",
    "    new_workbook = copy(workbook)  # 拷贝原工作簿\n",
    "    new_worksheet = new_workbook.get_sheet(0)  # 获取转化后工作簿中的第一个表格\n",
    "    for i in range(0, len(value)):\n",
    "        for j in range(len(value[0])):\n",
    "            new_worksheet.write(i+1, j, float(value[i][j]))# 从第二行开始(row=i+1)，按对应的行和列向表格中写入数据\n",
    "    new_workbook.save(path)  # 保存工作簿"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "8T-XP7dnzkQr"
   },
   "outputs": [],
   "source": [
    "path       = r'step2-BiLSTM.xls' \n",
    "sheet_name = 'NOx'\n",
    "title      = ['BiLSTM','y_test'] #表头\n",
    " \n",
    "excel_create(path, sheet_name) #新建表格\n",
    "excel_write_title(path, title) #写入表头\n",
    "#写入需要的一维数组(lat,lon,nox是之前已有的一维数组）\n",
    "excel_write_array(path, ypred_test,0)\n",
    "excel_write_array(path, y_test,1)\n"
   ]
  },
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